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敌对转移的数据辅助软传感器用于增强多级质量预测
Yun Dai1, Chao Yang2, Jialiang Zhu1
1Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
ACS omega
|June 12, 2023
概括
一种新的即时对抗转移学习 (JATL) 软传感方法改善了多级化学过程的预测. 这种方法通过调整分布和选择相关数据来提高新年级的表现,使用有限的数据.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 机器学习 机器学习
背景情况:
- 转移学习软传感器在多级化学过程中表现有前途.
- 高预测性能需要足够的目标域数据,通常无法用于新等级.
- 单个全球模型难以捕捉多级过程变量中的复杂关系.
研究的目的:
- 开发一种新的软传感方法,用于增强多级化学过程预测.
- 为应对转移学习中的新操作等级数据稀缺的挑战.
- 改进不同级别的过程变量关系的表征.
主要方法:
- 实施了对抗性转移学习 (ATL) 策略,以尽量减少等级之间的分配差异.
- 采用了即时学习 (JIT) 方法,从转移的源数据中选择相关数据.
- 为质量预测开发了一个即时对抗转移学习 (JATL) 软传感框架.
主要成果:
- 该JATL方法成功地减少了运行等级之间的分配差异.
- 可靠的软传感器模型是使用JIT从传输数据中选择数据来构建的.
- 新目标等级的质量预测可以实现,而不需要标记数据.
结论:
- 在多级化学过程中,JATL软传感方法显著提高了预测性能.
- 这种方法有效地克服了新年级数据稀缺性的局限性.
- 在两个化学工艺上的实验验证证证了JATL方法的优越性.
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